A stochastic map for uncertain spatial relationships
Proceedings of the 4th international symposium on Robotics Research
Introduction to algorithms
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing
International Journal of Robotics Research
Consistent, convergent, and constant-time SLAM
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Sparse Local Submap Joining Filter for Building Large-Scale Maps
IEEE Transactions on Robotics
Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision
IEEE Transactions on Robotics
Smoothing-based submap merging in large area SLAM
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
LESS-mapping: Online environment segmentation based on spectral mapping
Robotics and Autonomous Systems
A graph-based hierarchical SLAM framework for large-scale mapping
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
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When solving the Simultaneous Localization and Mapping (SLAM) problem, submapping and graphical methods have shown to be valuable approaches that provide significant advantages over the standard EKF solution: they are faster and can produce more consistent estimates when using local coordinates. In this paper we present CI-Graph, a submapping method for SLAM that uses a graph structure to efficiently solve complex trajectories reducing the computational cost. Unlike other submapping SLAM approaches, we are able to transmit and share information through maps in the graph in a consistent manner by using conditionally independent submaps. In addition, the current submap always summarizes, without further computations, all information available making CI-Graph be an intrinsically "up to date" algorithm. Moreover, the technique is also efficient in memory requirements since it does not need to recover the full covariance matrix. To evaluate CI-Graph performance, the method has been tested using a synthetic Manhattan world and Victoria Park data set.